Data Table
Top 10 Words for each Game
wordcloud <- dat %>%
select(gameId, comment_clean) %>%
unnest_tokens(word, comment_clean) %>%
anti_join(my_stop_words, by = 'word') %>%
count(word, gameId, sort = T) %>%
ungroup() %>%
group_by(word, gameId) %>%
summarise(freq = sum(n)) %>%
arrange(gameId,desc(freq))
DT::datatable(wordcloud %>% group_by(gameId) %>% top_n(10, freq), options = list(pageLength=10))
wordcloud %>%
group_by(gameId) %>%
top_n(10, freq) %>%
ungroup() %>%
mutate(word = reorder(word, freq)) %>%
ggplot(aes(word, freq, fill = gameId)) +
geom_col(show.legend = F) +
scale_color_brewer(type = 'div',palette = 'Reds') +
labs(x=NULL, y ='Frequency') +
facet_wrap(~gameId, scales = 'free') +
coord_flip()

Wordcloud
Cities Skylines
wordcloud %>%
filter(gameId == 'Cities_skylines') %>%
select(word, freq) %>%
with(wordcloud(word, freq, rot.per = 0.25, max.words = 200, colors=brewer.pal(12, 'Paired'), random.order = FALSE))

GTA V
wordcloud %>%
filter(gameId == 'GTA5') %>%
select(word, freq) %>%
with(wordcloud(word, freq, rot.per = 0.25, max.words = 200, colors=brewer.pal(12, 'Paired'), random.order = FALSE))

PUBG
wordcloud %>%
filter(gameId == 'pubg') %>%
select(word, freq) %>%
with(wordcloud(word, freq, rot.per = 0.25, max.words = 200, colors=brewer.pal(12, 'Paired'), random.order = FALSE))

DOTA2
wordcloud %>%
filter(gameId == 'dota') %>%
select(word, freq) %>%
with(wordcloud(word, freq, rot.per = 0.25, max.words = 200, colors=brewer.pal(12, 'Paired'), random.order = FALSE))

Civil VI
wordcloud %>%
filter(gameId == 'Civilization') %>%
select(word, freq) %>%
with(wordcloud(word, freq, rot.per = 0.25, max.words = 200, colors=brewer.pal(12, 'Paired'), random.order = FALSE))

Rocket League
wordcloud %>%
filter(gameId == 'Rocket_League') %>%
select(word, freq) %>%
with(wordcloud(word, freq, rot.per = 0.25, max.words = 200, colors=brewer.pal(12, 'Paired'), random.order = FALSE))

TF_IDF